1. AI Prompt Engineer / LLM Interaction Designer
Role Overview
The Prompt Engineer / LLM Interaction Designer is responsible for engineering reliable, controllable, and task-effective interactions between humans, systems, and large language models. The role focuses on translating ambiguous user intent and business requirements into structured prompts, schemas, and interaction flows that yield predictable, evaluable outputs across contexts and workloads. This is not copywriting. It is applied interface engineering for probabilistic systems.
Pre-requisite
Qualification
- Bachelor’s degree in Engineering, Computer Science, Mathematics, Cognitive Science, Linguistics, or related discipline
- Advanced degree is optional; demonstrated applied capability outweighs credentials
Experience
- 2–5 years in software engineering, applied NLP, UX for complex systems, or AI product roles
- Hands-on experience working with production LLM APIs (OpenAI, Anthropic, Google, etc.)
- Prior exposure to prompt iteration under real user or system load is mandatory
Mandatory Skills
- Prompt structuring techniques (instruction hierarchy, role framing, constraints, output schemas)
- Few-shot and zero-shot design with failure-aware prompting
- Structured outputs (JSON, XML, function/tool calling formats)
- Context window management and prompt compression strategies
- Understanding of LLM failure modes: hallucination, instruction drift, verbosity, mode collapse
- Ability to design prompts for evaluation, reproducibility, and versioning
- Basic scripting (Python or equivalent) to test, log, and iterate prompts systematically
Nice to Have Skills
- Experience with RAG-based prompting and retrieval-aware prompt design
- Familiarity with agent frameworks and multi-step prompt chaining
- Exposure to UX writing or human–computer interaction (HCI) principles
- Experience designing prompts for regulated or high-stakes domains
- Knowledge of prompt observability, A/B testing, and evaluation harnesses
- Familiarity with safety-aligned prompting and policy-constrained generation
2. LLM Application Engineer
Role Overview
The LLM Application Engineer builds production-grade applications where LLMs are embedded as functional components, not demos or chat toys. The role sits at the intersection of backend engineering, AI integration, and product logic—responsible for turning model capabilities into reliable, scalable, and testable software systems.
This role exists because LLMs alone do nothing useful. Value is created only when they are orchestrated with APIs, data stores, business rules, and user workflows.
Pre-requisite
Qualification
- Bachelor’s degree in Computer Science, Engineering, or equivalent technical discipline
- Formal ML degree is not mandatory; strong software engineering background is required
Experience
- 3–7 years of experience in backend or full-stack software engineering
- Hands-on experience integrating LLM APIs into real applications
- Experience shipping production systems with external dependencies and SLAs
Mandatory Skills
- Strong backend development skills (Python mandatory; Node/Java optional)
- LLM API integration (prompting, tool/function calling, streaming responses)
- Application-layer orchestration (request lifecycle, retries, fallbacks, caching)
- Designing structured outputs and validation pipelines
- Context assembly from multiple sources (user state, data, memory, retrieval)
- Error handling for non-deterministic model behavior
- API design, authentication, rate limiting, and cost controls
- Logging, tracing, and basic evaluation of LLM outputs
Nice to Have Skills
- Experience with RAG pipelines and vector databases
- Familiarity with async processing, queues, and background workers
- Exposure to frontend integration (React, chat interfaces, copilots)
- Experience with containerization and cloud deployment
- Knowledge of prompt evaluation frameworks and automated testing
- Familiarity with agentic workflows and multi-step reasoning chains
3. AI Workflow Automation Engineer
Role Overview
The AI Workflow Automation Engineer designs and implements end-to-end automated workflows where LLMs, tools, and enterprise systems operate together with minimal human intervention. The role focuses on replacing brittle, rule-based automation with adaptive, AI-driven process execution that can handle ambiguity, exceptions, and evolving inputs.
This is not RPA with prompts. It is AI-native orchestration across systems, data, and decisions.
Pre-requisite
Qualification
- Bachelor’s degree in Engineering, Computer Science, Information Systems, or equivalent
- Certifications in automation platforms or cloud systems are acceptable substitutes
Experience
- 3–6 years in workflow automation, backend engineering, platform engineering, or systems integration
- Hands-on experience automating multi-step business or operational processes
- Direct exposure to LLM-powered automation or AI-augmented workflows is required
Mandatory Skills
- Designing multi-step workflows with conditional logic and state management
- Integration of LLMs into automation pipelines (decisioning, classification, extraction)
- API-based system integration (SaaS tools, internal services, databases)
- Orchestration tools and patterns (event-driven flows, schedulers, queues)
- Error handling, retries, human-in-the-loop escalation paths
- Understanding of workflow observability, logging, and auditability
- Cost, latency, and reliability optimization for long-running automations
- Scripting/programming (Python mandatory)
Nice to Have Skills
- Experience with no-code/low-code automation platforms (Zapier, n8n, Make, Power Automate)
- Familiarity with agent-based automation or task-planning systems
- Exposure to RPA modernization or legacy automation migration
- Knowledge of business process modeling (BPMN or equivalent)
- Experience building automations in regulated or compliance-heavy environments
- Understanding of prompt versioning and workflow evaluation metrics
4. No-Code / Low-Code AI Automation Specialist
Role Overview
The No-Code / Low-Code AI Automation Specialist enables rapid deployment of AI-powered automations using visual, declarative, and configuration-driven platforms. The role focuses on time-to-value, allowing non-engineering teams to leverage LLMs and AI services safely within predefined guardrails.
This role is not about bypassing engineering. It is about operationalizing AI at scale without writing full custom systems, under architectural and governance constraints.
